Evidence map›Paper›PMID 29554467›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018

Contribution of Breast Cancer to Overall Mortality for US Women.

Ronald E Gangnon, Natasha K Stout, Oguzhan Alagoz, John M Hampton, Brian L Sprague, Amy Trentham-Dietz

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  12. Population simulation modeling of disparities in US breast cancer mortality.Journal of the National Cancer Institute. Monographs · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ronald E GangnonDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Natasha K StoutDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Oguzhan AlagozDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.
John M HamptonDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Brian L SpragueDepartment of Surgery and University of Vermont Cancer Center, Burlington, VT, USA.
Amy Trentham-DietzDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Nihal Ahmad · 1985 to 2026
$142.6M
Comparative Modeling: Informing Breast Cancer Control Practice and PolicyU01CA199218 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2015 to 2019
$9.0M
Comparative Modeling: Informing Breast Cancer Control Practice & PolicyU01CA152958 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2010 to 2015
$7.6M
NCI NIH HHS P30 CA014520NCI NIH HHS U01 CA152958NCI NIH HHS U01 CA199218
6 · The paper itself

Abstract

objectiveBreast cancer simulation models must take changing mortality rates into account to evaluate the potential impact of cancer control interventions. We estimated mortality rates due to breast cancer and all other causes combined to determine their impact on overall mortality by year, age, and birth cohort.

methodsBased on mortality rates from publicly available datasets, an age-period-cohort model was used to estimate the proportion of deaths due to breast cancer for US women aged 0 to 119 years, with birth years 1900 to 2000. Breast cancer mortality was calculated as all-cause mortality multiplied by the proportion of deaths due to breast cancer; other-cause mortality was the difference between all-cause and breast cancer mortality.

resultsBreast cancer and other-cause mortality rates were higher for older ages and birth cohorts. The percent of deaths due to breast cancer increased across birth cohorts from 1900 to 1940 then decreased. Among 50-year-old women, in the 1920 birth cohort, 52 (9.9%) of 100,000 deaths (95% CI, 9.8% to 10.1%) were attributed to breast cancer whereas 476 of 100,000 were due to other causes; in the 1960 birth cohort, 22 (8.5%) of 100,000 deaths (95% CI, 8.3% to 8.7%) were attributed to breast cancer with 242 of 100,000 deaths due to other causes. The percentage of all deaths due to breast cancer was highest (4.1% to 12.9%) for women in their 40s and 50s for all birth cohorts.

conclusionsThis study offers evidence that advances in breast cancer screening and treatment have reduced breast cancer mortality for women across the age spectrum, and provides estimates of age-, year- and birth cohort-specific competing mortality rates for simulation models. Other-cause mortality estimates are important in these models because most women die from causes other than breast cancer.

Indexed as

AdolescentAdultAgedAged, 80 and overAge DistributionBreast NeoplasmsCause of DeathChildChild, PreschoolCohort StudiesComputer SimulationFemaleHumansInfantLogistic ModelsMiddle Agedage-period-cohort modelingbreast cancermortalitysimulation modeling

Identifiers

PMID29554467
PMCPMC5963706

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.